Q_all_memory

beast.fitting.fit.Q_all_memory(prev_result, obs, sedgrid, obsmodel, qnames_in, p=[16.0, 50.0, 84.0], gridbackend='cache', max_nbins=200, stats_outname=None, pdf1d_outname=None, pdf2d_outname=None, pdf2d_param_list=None, grid_info_dict=None, lnp_outname=None, lnp_npts=None, save_every_npts=None, threshold=-40, resume=False, use_full_cov_matrix=True, do_not_normalize=False)

Fit each star, calculate various fit statistics, and output them to files. All done in one function for speed and ability to resume partially completed runs.

Parameters:

prev_resultdict

previous results to include in the output summary table usually basic data on each source

obsObservation object instance

observation catalog

sedgridstr or grid.SEDgrid instance

model grid

obsmodelbeast noisemodel instance

noise model data

qnameslist

names of quantities

parray-like

list of percentile values

gridbackendstr or grid.GridBackend

backend to use to load the grid if necessary (memory, cache, hdf) (see beast.core.grid)

max_nbinsint (default=200)

maxiumum number of bins to use for the 1D likelihood calculations

save_every_nptsint

set to save the files below (if set) every n stars a requirement for recovering from partially complete runs

resumebool

set to designate this run is resuming a partially complete run

use_full_cov_matrixbool

set to use the full covariance matrix if it is present in the noise model file

stats_outnamestr

set to output the stats file into a FITS file with extensions

pdf1d_outnamestr

set to output the 1D PDFs into a FITS file with extensions

pdf2d_outnamestr

set to output the 2D PDFs into a FITS file with extensions

pdf2d_param_listlist of strs or None

set to the parameters for which to make the 2D PDFs

grid_info_dictdict

Set to override the mins/maxes of the 1dpdfs, and the number of unique values

lnp_outnamestr

set to output the sparse likelihoods into a (usually HDF5) file

thresholdfloat

value above which to use/save for the lnps (defines the sparse likelihood)

lnp_nptsint

set to a number to output a random sampling of the lnp points above the threshold. Otherwise, the full sparse likelihood is output.

do_not_normalize: bool

Do not normalize the prior weights before applying them. This should have no effect on the final outcome when using only a single grid, but is essential when using the subgridding approach.

Returns:

N/A